Photovoltaic Panels Extraction under Evolutionally Algorithms: A Comparative Study Energy and Power... · 2015-11-12 · Photovoltaic Panels Extraction under Evolutionally Algorithms:

Photovoltaic Panels Extraction under Evolutionally Algorithms:

A Comparative Study

I. Benouareth*1, K. Khelil

2 and I. Abadlia

3

*1,2Unv. Souk Ahras, Fac. Sci & Tec. LEER, Lab. Souk Ahras, Algeria.

3Unv. Badji Mokhtar Annaba, Fac. engineering. LASA Lab. Annaba, Box 12, 23000, Algeria.

Email: [email protected]

Abstract –The center of the intention of this paper is the

extraction of photovoltaic panel’s parameters, using

Evolutionally Algorithms (EAs). PV panels are

optimized via genetic algorithm and particle swarm

optimization techniques at different atmospheric

conditions and various parameters whereas panels of

constructor are prepared in ideal conditions. So, the

object of the present optimization is too accurate

extraction of PV panel parameters in different

conditions via objective function. A comparative study

between proposed optimization methods and a real

modal panel of constructor is realized and results are

obtained by MATLAB.

Keywords – Photovoltaic Panels, Extraction,

Evolutionally Algorithms, Genetic Algorithms,

Particle Swarm Optimization, Objective function

I. INTRODUCTION

Renewable energy sources are getting more

attention in recent years as alternative means of

generating electricity in various parts of the world.

Various motivations are promoting serious

contribution of environmental pleasant (friendly)

energy sources in mass electricity production in many

countries. Some of these reasons are: environmental

concerns due to greenhouse effect, possible reduction

and price increase of conventional energy primary

resource.

Solar energy is one of the most promising emission

free resources that are currently being used all over

the world to (contribute) supply the rising demands of

electric power. Solar photovoltaic is the fastest

growing power-generation technology in the world

with an annual average increase of 60% between

2004-2009 [1]. The traditional extraction methods

were based on direct approaches on the use of I-V

curve features such as axis intercepts and the

gradients at selected points, to determine some cell

parameters. However, accuracy of these techniques

was limited due to nonlinearity of measured I-V data,

multi variable and multi modal problem which have

many local optimal [1] and [2]. The recent developed

algorithms are based on nature motivated ideas, such

as ant a colony optimization, evolutionary algorithms,

and a particle swarm optimization etc. [3].

Most of these algorithms are meta-heuristic and

they may be applied to a large variety of problems. In

a similar context, Artificial Bee Colony (ABC)

algorithm was initially proposed by Karaboga in 2005

as a technical report for numerical optimization

problem [4]. Artificial Bee Colony Algorithm (ABC)

is nature-inspired meta-heuristic, which imitates the

foraging behavior of bees.

Furthermore, the best EA method is validated with

six PV modules of different types (multicrystalline,

mono-crystalline, and thin-film) from various

manufacturers. Finally a table is proposed to compare

the performance of each method; although the table

may not be appropriated for benchmarking. It can be

used as a guideline to indicate the best EA method to

extract the parameters of a one diode PV cell model.

II. MATHEMATICAL MODEL OF PV PANEL

Despite the fact that the diffusion and

recombination currents are linearly independent, it is

possible to combine them together under the

introduction of a nonphysical diode ideality factor n.

The use of this single diode model, to describe the

static I-V characteristic, has recently been considered

widely, it has also been used successfully to fit

experimental data. The single diode model equivalent

circuit is shown in Fig. 1 [5].

Fig. 1: Equivalent circuit of a single diode model.

In this model, Eq. (1) is reduced to the following

equation

𝐼 = 𝐼𝑝ℎ − 𝐼𝑜 [𝑒𝑥𝑝 (𝑉 + 𝐼𝑅𝑠

𝑛𝑉𝑇

) − 1] −𝑉 + 𝐼𝑅𝑠

𝑅𝑠ℎ

(1)

The photovoltaic panel comprising 𝑁𝑆cells in

series, assume that all cells are identical and are

exposed to the same temperature and a uniform

illumination𝐼𝑝𝑎𝑛𝑛𝑒𝑎𝑢 = 𝐼𝑐𝑒𝑙𝑙𝑢𝑙𝑒and𝑉𝑝𝑎𝑛𝑛𝑒𝑎𝑢 = 𝑁𝑆. 𝑉𝑐𝑒𝑙𝑙𝑢𝑙𝑒

In this work, we use the three photovoltaic panels are

[6]: MSX PV-60, MSX-64 and KyoceraKG200GT.

Under standard test conditions (air mass 1.5, cell

temperature = 25°𝐶 and irradiation = 1000𝑊/𝑚2

are given in Table I where IPV is the current generated

by the incidence of light; Io is the reverse saturation

currents of diode. The Io term is introduced to

International Conference on Automatic control, Telecommunications and Signals (ICATS15)University BADJI Mokhtar - Annaba - Algeria - November 16-18, 2015

1

compensate (for) the recombination loss in the

depletion region as described in Chih-Tang et al.

(1957). Other variables are defined as follows: VT is

equivalent to (Ns*k *T/q) which are the thermal

voltages of the PV module having Ns cells connected

in series, q is the electron charge (1.60217646.10-19

C), k is the Boltzmann constant (1.3806503. 10-23

J/K)

and T is the temperature of the P_N junction in

Kelvin

Table I: Electrical characteristics in STC

KG200GT MSX-64 MSX-60

maximum power

(𝑃𝑚𝑎𝑥) 200W 64W 60W

Peak voltage

(𝑉𝑚𝑝) 26.312V 17.5V 17.1V

Peak current

(𝐼𝑚𝑝) 7.61A 3.66A 3.5A

Short-circuit current

(𝐼𝑆𝐶) 8.21A 4.0A 3.8A

Open circuit voltage

(𝑉𝑂𝐶) 32.9V 21.3V 21.1V

Temperature Coefficient

of 𝑉𝑂𝐶 (−𝟖𝟎 ± 𝟏𝟎)𝒎𝑽/°𝑪


of 𝐼𝑆𝐶 (𝟎. 𝟎𝟎𝟔𝟓 ± 𝟎. 𝟎𝟏𝟓)%/°𝑪


of 𝑃𝑚𝑎𝑥 −(𝟎. 𝟓 ± 𝟎. 𝟎𝟓)%/°𝑪

cells Number 36

III. BRIEF OVERVIEW OF EVOLUTIONALLY

ALGORITHMS

A) Genetic algorithm

The genetic algorithm (GA) is based on the theory

of biological evolution (Holland, 1975). Fig. 2 shows

the flow chart of GA. The common operators used in

GAs are described as follows [7-9]:

Selection

This procedure selects the chromosomes that

contribute in the reproduction process to give birth to

the next generation. Only the best chromosomes are

considered for the next generation. The selection

process can be realized by various techniques,

including the elitist model, the ranking model, the

roulette wheel procedure (Haupt and Haupt, 2004),

etc.

Mutation

It introduces changes in some genes (parameters)

of a chromosome in a population. This procedure is

performed by GAs to explore new solutions. Random

mutations modify a small percentage of the

population except for the best chromosomes. A

mutation rate between 1% and 20% often obtain

better results. If the mutation rate is above 20%, many

good parameters can be mutated, and causing a pause

the algorithm. Note that the new value of each

parameter should be in the [𝑋𝐼𝐿,𝑋𝐼𝐻] corresponding

interval. Consequently, after paring, mutated

parameters are engaged to ensure that the parameters

space is explored in new regions.

Crossover

This process uses two selected chromosomes from

a current generation (parents) and crosses them with

some probability to obtain two individuals for the new

generation. There are several types of crossover, but

the simplest method is arbitrarily to choose one or

more parameters in the chromosome of each parent, to

mark as crossover points. Then, the criteria between

these points are merely swapped between the two

parents. Although GA has been used extensively in

many applications, numerous computational

difficulties, namely premature convergence, low

speed, and degradation for highly interactive fitness

function are reported (Zwe-Lee, 2004; Ji et al., 2006).

Fig. 2: Flow chart of GA.

B) Particle swarm optimization

Particle swarm optimization (PSO) is a stochastic

population-based search method, modeled after the

behavior of bird flocks (Eberhart and Kennedy, 1995;

Kennedy and Eberhart, 1995) [10-12].

A PSO algorithm maintains a swarm of individuals

called particles where each ones represents a

candidate solution.

Particles follow a simple behavior: emulate the

success of neighboring particles, and achieve their

own achievement. The position of a particle is

therefore influenced by the best particle in a

neighborhood.

The velocity is calculated by:


2

𝒗𝒊(𝒕 + 𝟏) = 𝝎 𝒗𝒊(𝒕) + 𝒄𝟏𝒓𝟏[𝒙𝒑𝒊(𝒕) − 𝒙𝒊(𝒕)]+ 𝒄𝟐𝒓𝟐[𝒈(𝒕) − 𝒙𝒊(𝒕)] (2)

Where 𝑾 is the inertia weight, 𝑪𝟏 and 𝑪𝟐 are the Well

as the best solution found by the particle. Particle

position, xi, are adjusted using: 𝒙𝒊 (𝒕) = 𝒙𝒊 (𝒕 − 𝟏) + 𝒗𝒊 (𝒕) (3) Where the velocity component, vi, represents the step

size. Acceleration coefficients, r1, r2 Є U (0, 1), (yi) it

is the own best position of particle i, and 𝒊 is the

neighborhood best position of particle i. The inertia

weight w plays an important role in balancing the

global research and local research. A large w

facilitates a global research while a small inertia

weight facilitates a local research. It can be a positive

constant or a positive decreasing linear function of

iteration index j. In (Ye et al., 2009), the inertia

weight was used from the following falling linear

function:

𝑾(𝒋) = (𝒘𝒎𝒂𝒙 − 𝒘𝒎𝒊𝒏)𝒋

𝑮𝒎𝒂𝒙 (4)

Where𝒘𝒎𝒂𝒙 and 𝒘𝒎𝒊𝒏 are the final weight and the

initial weight, respectively, and 𝐺𝑚𝑎𝑥 is the maximum

iteration number

The velocity is further updated by following law

(5)

Where𝑉𝑚𝑎𝑥 is a constant that it is set to clamp the

unnecessary wandering of particles. The choice of

𝑉𝑚𝑎𝑥 usually is equivalent to the maximum acceptable

departure of any particle in that dimension (Shi and

Eberhart, 1998). Fig. 3 depicts the flow chart of the

PSO. Due to excessive roaming of particles, high

convergence time and great number of iterations are

experienced (Ye et al., 2009).Furthermore, penalizing

unnecessary movement of particles can affect the

convergence performance. For instance, in a PSO

based PV cell parameters extraction (Ye et al., 2009),

the authors have used a common approach to penalize

the velocity of the particles with a factor𝑉𝑚𝑎𝑥. The

global exploration ability of a PSO strongly depends

on this factor. If 𝑉𝑚𝑎𝑥 is too large, particles may

reveal good and satisfactory solutions. Otherwise a

small value of 𝑉𝑚𝑎𝑥 will end the particles to go

beyond locally good solutions. Hence, the choice of

𝑉𝑚𝑎𝑥may not be consistent for different types of PV

modules.

Fig. 3: Flow chart of PSO.

IV. EXTRACTING MODEL PARAMETERS AND

PROBLEM FORMULATION

Evolutionary algorithms provide an efficient and

improved optimization for non-convex problems.

Generally, the cost functions used in the estimation of

electrical model parameters are not convex.

Therefore, evolutionary algorithms are expected to

offer better performance than conventional

optimization techniques. In our work, we adopted the

genetic algorithm (GA) and particle swarm

Optimization (PSO) and differential evolution (DE)

for this type of estimation problem. The objective

function used in the algorithm is to solve the

(𝟏)equation to determine the five parameters in

question. These parameters will be used to predict the

values given by the manufacturer, ie the short-circuit

current 𝐼𝑠𝑐 the open circuit voltage𝑉𝑜𝑐 , the maximum

power 𝑃𝑚𝑎𝑥thevoltage at the maximum power𝑉𝑚𝑝and

current of maximum power𝐼𝑚𝑝. Thus the optimization

problem is minimized the cost function is the sum of

quadratic errors

𝑱 = (𝑰𝒔𝒄 − 𝒔𝒄)𝟐+ (𝑽𝒐𝒄 − 𝒐𝒄)

𝟐+ (𝑷𝒎𝒂𝒙 − 𝒎𝒂𝒙)

𝟐

+ (𝑽𝒎𝒑 − 𝒎𝒑)𝟐+ (𝑰𝒎𝒑 − 𝒎𝒑)

𝟐 (6)

Isc, Voc, Pmax, Vmp, ImpWhere the predicted values

of short circuit are current, open circuit voltage,

maximum power, maximum power voltage and

current at maximum power respectively.

A. Performance of the genetic algorithm (GA) and

particle swarm (PSO)

Using the data in Table I from the data sheet

provided by the manufacturer of photovoltaic panels

in question, 𝑃𝑚𝑎𝑥 , 𝑉𝑚𝑝 , 𝐼𝑚𝑝 , 𝐼𝑆𝐶 , 𝑉𝑂𝐶 namely the five

values, the five parameters 𝐼𝑝ℎ , 𝐼𝑜 , 𝑅𝑠, 𝑅𝑠ℎ, 𝑛 forming

chromosome are estimated. The tables II and III

present the parameters of algorithm GA and PSO

respectively.

TABLE II: PARAMETERS OF THE GENETIC ALGORITHM.

Parameter’s Value’s

Population size 100

variables Number 5

Maximum generations 100

Selection stochastic

Crossover dispersed

crossing Probability 0.8

Mutation Gaussian

Elite Count Data 20

TABLE III: PARAMETERS OF THE PSO.

Parameter’s Value’s

Population size 100

Variables Number 5

Maximum generations 100

C1 2

C2 2

W 0.3

B. Optimization Steps

1. Initial population

A random initial population chromosome is


3

generated. Each chromosome is a string in the

following form𝐼𝑝ℎ 𝐼𝑜 𝑅𝑠 𝑅𝑠ℎ 𝑛. Thus, in

our optimization, the initial population is a real

valued (matrix) environment. Following a

research of the literature related to the field of

modeling, intervals of five parameters are chosen

as follows:

𝐼𝑠𝑐 − 1 ≤ 𝐼𝑝ℎ ≤ 𝐼𝑠𝑐 + 1

10−8 ≤ 𝐼𝑜 ≤ 10−7

0.05 ≤ 𝑅𝑠 ≤ 2

100 ≤ 𝑅𝑠ℎ ≤ 1000

1 ≤ 𝑛 ≤ 1.5

2. Solve the nonlinear equation:

𝐼 = 𝐼𝑝ℎ − 𝐼𝑜 [𝑒𝑥𝑝 (𝑉 + 𝐼𝑅𝑠

𝑛𝑉𝑇) − 1] −

𝑉 + 𝐼𝑅𝑠

𝑅𝑠ℎ

Each chromosome is replaced in the equation

above. The equation is solved using the Newton

Raphson method for solving non-linear equations to

determine the characteristics I (V) and P (V) of the

module.

3. Calculation of parameters

From I-V and P-V characteristics determined in

step 3: the Values of Pmax, Vmp, Impare extracted

from the power vector P=V*I the values Isc, Vocare

calculated by interpolation.

𝒔𝒄, 𝒐𝒄, 𝒎𝒂𝒙, 𝒎𝒑, 𝒎𝒑

4. Evaluation of the objective function:

Evaluate the cost (or fitness) of each chromosome

in the population. The objective function (or cost)

assigns each individual (chromosome) of the

population a numerical value reflecting its quality as a

potential solution. The cost defines the capacity of the

individual (chromosome) to survive and produce

offspring. In our case, the objective function is the

quadratic error given by equation

V. VALIDATION AND RESULTS OF

SIMULATION

In order to validate our model, all

photovoltaic panels are measured (See Table I for the

characteristics). The method, based on the

optimization is to extract the five parameters of the

model 𝐼𝑝ℎ 𝐼𝑜 𝑅𝑠 𝑅𝑠ℎ 𝑛 minimizing the square

error (EQ) 𝑃𝑚𝑎𝑥 , 𝑉𝑚𝑝 , 𝐼𝑚𝑝, 𝐼𝑆𝐶 , 𝑉𝑂𝐶 between the

values given by the manufacturer and the values

Isc, Voc, Pmax, Vmp, Imp estimated. To check the

accuracy, the fidelity and to obtain better solutions,

algorithms are executed toward 100 times and all five

parameters. Giving the value of the smallest square

error is considered to be the most optimal solution to

the problem. The results are shown in

Table.IVandTable.Vfor the different panels.

TABLEIV.VALUES OF FIVE PARAMETERS ESTIMATED BY THE GA

TABLE V VALUES OF FIVE PARAMETERS ESTIMATED BY THE PSO

Using the estimated values shown (in the tables

shown) in Table IV and Table V, the five parameters

𝑃𝑚𝑎𝑥 , 𝑉𝑚𝑝 , 𝐼𝑚𝑝 , 𝐼𝑆𝐶 , 𝑉𝑂𝐶 are calculated and compared

with those given by the manufacturers as illustrated

by Table VI, Table VI,and Table VI for photovoltaic

panels. The quadratic error (6) is calculated to

appreciate better the combination of our values with

those provided by the manufacturer itself.

Table VI Comparison of five parameters given by the

manufacturer of MSX-60 with those extracted from the

model proposed by (GA) and (PSO) MSX-60

Manufactu

rer

GA PSO

Maximum

power𝑷𝒎𝒂𝒙 [𝑾] 60 60.076 60.0288

Peak voltage 𝑽𝒎𝒑 [𝑽] 17.1 16.792 17.0667

Peak current (𝑰𝒎𝒑) [𝑨] 3.5 3.5775 3.5173

𝑆ℎ𝑜𝑟𝑡_𝑐𝑖𝑟𝑐𝑢𝑖𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑰𝑺𝑪 [𝑨] 3.8 3.9520 3.8000

Open circuit

voltage 𝑽𝑶𝑪[𝑽] 21.1 21.326 21.0826

square error 0.0705 0.0012

Table VII Comparison of five parameters given by the

manufacturer of MSX-64 with those extracted from the

model proposed by (GA) and (PSO) MSX-64

manufactu

rer

GA PSO

Maximum power𝑃𝑚𝑎𝑥 [𝑊] 64 64.110 64.034

Peak voltage 𝑉𝑚𝑝 [𝑉] 17.5 17.336 17.462

Peak current (𝐼𝑚𝑝) [𝐴] 3.66 3.6980 3.6670

𝑆ℎ𝑜𝑟𝑡_𝑐𝑖𝑟𝑐𝑢𝑖𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝐼𝑆𝐶 [𝐴] 4 3.9983 3.9972

Open circuit voltage 𝑉𝑂𝐶[𝑉] 21.3 21.283 21.271


Parameters KG200GT MSX-60 MSX-64

Photocurrent

𝑰𝒑𝒉[A] 8.2986 3.8039 4.0029

saturation current

𝑰𝒐[A] 2.374×10_8 4.998x10−8 9.379x10−8

𝑹𝒔[Ω]

series resistance 0.21072 0.2220 0.1356

parallel resistance

𝑹𝒑[Ω] 123.2693 365.8308 221.4217

𝒏

Ideality factor 1.2078 1.2759 1.3139

Parameters KG200GT MSX-60 MSX-64

Photocurrent

𝑰𝒑𝒉 [A] 8.2220 3.8203 4.0013

saturation current

𝑰𝒐[A] 3.110×10_8 5.93x10−8 5.604x10−8

𝑹𝒔[Ω]

series resistance 0.2709 0.2221 0.1578

parallel resistance

𝑹𝒑[Ω] 741.5098 376.6715 223.7207

𝒏

Ideality factor 1.2241 1.2716 1.2762


4

Table VIII Comparison of five parameters given by the

manufacturer of KyoceraKG200GTwith those extracted from the

model proposed by (GA) and (PSO)

KG200GT

manufacturer GA PSO

Maximum

power𝑃𝑚𝑎𝑥 [𝑊] 200 199.92 200.06

Peak voltage 𝑉𝑚𝑝 [𝑉] 26.312 26.593 26.231

Peak current (𝐼𝑚𝑝) [𝐴] 7.61 7.4802 7.6268

𝑆ℎ𝑜𝑟𝑡_𝑐𝑖𝑟𝑐𝑢𝑖𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝐼𝑆𝐶 [𝐴] 8.21 8.1956 8.2070

Open circuit

voltage 𝑉𝑂𝐶[𝑉] 32.9 32.844 32.890


Based on the proposed model and its five

parameters identified by the optimization algorithms

characteristics IV, curves are plotted and compared

with the curves given in the data sheets of the

manufacturer of photovoltaic modules. Fig. 4, Fig. 5

and Fig.6 illustrate the IV characteristics given by the

manufacturer and the model proposed for both

photovoltaic panels considered. From these figures, it

is easy to notice that the characteristics obtained from

optimized model are nearly identical to those given by

the manufacturer.

Fig. 4: characteristics I-V of the MSX 60 module by the

manufacturer.

Fig. 5: characteristics I-V of the MSX 64 module by GA

Fig. 6: characteristics I-V of the MSX 60 module by PSO

Through modeling it is possible to predict the

behavior of the module, and consequently the

photovoltaic panels, taking into account the changes

in environmental parameters such as temperature and

sunshine. To illustrate this advantage, we have drawn,

for the MSX-60 module, PV characteristics (Fig. 7)

for different temperatures and IV characteristics for

different radiation (Fig.8).

Fig. 7: characteristics P-V of the MSX 60 module by GA and PSO

respectively for different temperatures

Fig. 8: characteristics I-V of the MSX 60 module by GA and PS

respectively for different irradiations

To compare the two techniques used to optimize we

can only focus on the characteristics of P (V) and I

(V). Thus, we adopted four criteria, namely the

accuracy of the solution, consistency, speed

convergence of the algorithm, and the number

parameters of control.

Accuracy of the solution

Fig.9, Fig.10 and Fig.11 illustrate the relative

errors between the estimated values and those given

by the manufacturer for PV modules MSX60, MSX64

and KG200GT respectively from the figures. The two

methods have fairly low relative errors. However the

PSO algorithm is more accurate than the GA

algorithm.

Consistency of solution

Fig.12EvenFig.16present the estimated values of

𝐼𝑝ℎ 𝐼𝑜 𝑅𝑠 𝑅𝑠ℎ 𝑛 versus 100 executions for

MSX60 module. Then (then) the Fig.17 shows the

value of the cost function based on 100 runs of the

algorithm. When we observe the figure, we find that

the solution obtained by the GA algorithm (and) is

(very random) unsystematic while the PSO algorithm

provides the best stability. To appreciate better the

difference between the three algorithms, the relative

standard deviation of the various methods is

calculated as illustrated by the table VIII. The results

of this Table confirm the superiority of the PSO

algorithm in terms of the consistency of the solution.

0 5 10 15 20 250

1

2

3

4

5

6

modèle : MSX64 Irradiation = 1000 W/m2

Tension [V]

Cou

rant

[A]

T = 0°C

T = 25°C

T = 50°C

T = 75°C

0 5 10 15 20 250

1

2

3

4

5

6


Tension [V]

Cou

rant

[A]

T = 0°C

T = 25°C

T = 50°C

T = 75°C

0 5 10 15 20 250

10

20

30

40

50

60

70

80

90


Tension [V]

Pu

issa

nce

[W

]

T = 0°C

T = 25°C

T = 50°C

T = 75°C

0 5 10 15 20 250

10

20

30

40

50

60

70

80

90


Tension [V]

Pu

issa

nce

[W

]

T = 0°C

T = 25°C

T = 50°C

T = 75°C

0 5 10 15 20 250

1

2

3

4

5

6

modèle : MSX60 Température de cellule = 25°C

Tension [V]

Co

ura

nt [A

] G = 1000W/m2

G = 800W/m2

G = 600W/m2

G = 400W/m2

G = 200W/m2

0 5 10 15 20 250

1

2

3

4

5

6

modèle : MSX60 Température de cellule = 25°C

Tension [V]

Co

ura

nt [A

] G = 1000W/m2

G = 800W/m2

G = 600W/m2

G = 400W/m2

G = 200W/m2


5

Fig.9: Relative error of the

Isc, Voc, Pmax, Vmp, Imprespectively of MSX-60.

Fig. 10: Relative error of the

Isc, Voc, Pmax, Vmp, Imp respectively of MSX-60

Fig. 11: Relative error of the

Isc, Voc, Pmax, Vmp, Imp respectively of MSX-60

Fig. 12: performance Convergence of Iph with PSO and GA

Fig.13: performance Convergence of Io with PSO and GA

Fig. 14: performance Convergence of Rs with PSO and GA

Fig. 15: performance Convergence of RP with PSO and GA

Fig. 16: performance Convergence of n with PSO and GA

Fig. 17: performance Convergence of the fitness with PSO and GA

Table VIII Comparison of five parameters given by the

Method

Parameters

GA PSO

Iph 0,28202% 0,00967%

Io 5,42e--14% 2,29e--14%

Rs 5,0271% 0,6009%

RP 40,8950% 18,1420%

N 0,5502% 0,1337%

Fitness (cost) 0.1624% 0,0043%

Speed of convergence

Figure 18 shows the convergence of the three

algorithms. According to the figure, it is clear that the

PSO slowly converges virtually to the GA algorithm

giving better solution.


6

Fig. 18: Speed of convergence

Number of control parameters

Four evolutionary methods have less Observation.

Controller’s parameters ease the tuning effort and

facilitate the formulation of the optimization problem.

- In GA: crossover rate, mutation factor, number of

Childs in elite strategy and migration factor

- In PSO: W is the inertia weight C1 and C2 are the

acceleration coefficients,

VI. CONCLUSION

In this paper, the extractions of parameters of three

PV modules are performed. We even compare the two

techniques, used in estimating parameterized PV

panels comparative, discover from the exact view

point uniformity of solution convergence of the

algorithm. The number of control parameters show

that the method PSO is relatively more efficient. The

relevant simulation results have been given by

Programming using MATLAB.

REFERENCES

[1] K. M. EL-NAGGAR, M. R. ALRASHIDI, M. F.

ALHAJRI, AND A. K. AL-OTHMAN, "SIMULATED

ANNEALING ALGORITHM FOR PHOTOVOLTAIC

NPARAMETERS IDENTIFICATION," SOLAR

ENERGY, VOL. 86, NO. 1, PP. 266-274, 2012.

[2] IEE-EUROPE PROGRAMME, RENEWABLE

ELETRICITY MAKE THE SWITCH PROJECT

REPORT, EXECUTIVE AGENCY

FORCOMPETITIVENESS AND INNOVATION OF THE

EUROPEAN COMMISSION, N°4, SEPTEMBER 2004.

[3] W. XIAO, M. G. J. LIND, W. G. DUNFORD, AND

A. CAPEL, "REAL-TIME IDENTIFICATION OF

OPTIMAL OPERATING POINTS IN PHOTOVOLTAIC

POWER SYSTEMS," IEEE TRANSACTIONS ON

INDUSTRIAL ELECTRONICS, VOL. 53, NO. 4, PP.

1017-1026, 2006.

[4] F. ADAMO, F. ATTIVISSIMO1, A. DI NISIO, A. M.

L. LANZOLLA, M. SPADAVECCHIA,

“PARAMETERS ESTIMATION FOR A MODEL OF

PHOTOVOLTAIC PANELS”, XIX IMEKO WORLD

CONGRESS FUNDAMENTAL AND APPLIED

METROLOGY SEPTEMBER 6−11, 2009, LISBON,

PORTUGAL.

[5] ASKARZADEH ALIREZA, REZAZADEH ALIREZA.

PARAMETER IDENTIFICATION FOR SOLAR CELL

MODELS USING HARMONY SEARCH BASED

ALGORITHMS. SOL ENERGY 2012;86(11):

3241E9.

[6] PRINTED ON RECYCLED PAPER1997 SOLAREX.

6059-8, PP.4/97.

[7] HOLLAND, J., 1975. ADAPTATION IN NATURAL

AND ARTIFICIAL SYSTEMS. MIT PRESS.

[8] ISHAQUE, K., SALAM, Z., ET AL., 2011. SIMPLE,

FAST AND ACCURATE TWO-DIODE MODEL FOR

PHOTOVOLTAIC MODULES. SOLAR ENERGY

MATERIALS AND SOLAR CELLS 95 (2), 586–594.

[9] JERVASE, J.A. ET AL., 2001. SOLAR CELL

PARAMETER EXTRACTION USING GENETIC

ALGORITHMS. MEASUREMENT SCIENCE AND

TECHNOLOGY 12 (11), 1922.

[10] EBERHART, R., KENNEDY, J., 1995. A NEW

OPTIMIZER USING PARTICLE SWARM THEORY.

MICRO MACHINE AND HUMAN SCIENCE, MHS

‘95. PROCEEDINGS OF THE SIXTH

INTERNATIONAL SYMPOSIUM ON.

[11] GA¨MPERLE, R., MU¨ LLER, S.D., ET AL.,

2002. A PARAMETER STUDY FOR DIFFERENTIAL

EVOLUTION. WSEAS INT. CONF. ON ADVANCES

IN INTELLIGENT SYSTEMS, FUZZY SYSTEMS,

EVOLUTIONARY COMPUTATION, PP. 293–298.

[12] KENNEDY, J., EBERHART, R., 1995. PARTICLE

SWARM OPTIMIZATION. NEURAL NETWORKS,

PROCEEDINGS OF IEEE INTERNATIONAL

CONFERENCE ON.


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